Intelligent Transport Systems (ITS) are advanced applications which, without embodying intelligence as such, aim to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and smarter use of transport networks. ITS strategies such as, for example, High Occupancy Vehicle (HOV)/High Occupancy Toll (HOT) are introduced to reduce traffic congestion and maintain the service level on a freeway. Optimization of multiple ITS strategies independently and simultaneously is a challenge and increasingly impossible without a systematic algorithm.
Conventionally microscopic traffic simulation models are employed to capture the dynamics on a freeway and to model such systems, but they do not provide optimization capability. Also, prior art approaches may have a conflicting effect on the outputs of the system and do not address multiple objectives such as maximizing throughput, maintaining traffic speeds, maximizing revenue, etc.
Based on the foregoing, it is believed that a need exists for an improved method and system for modeling and optimizing multiple ITS strategies utilizing a systematic genetic algorithm, as will be described in greater detail herein.